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Population in Zürich

Zürich Statistical Office collects data on city and its residents. This data is published as Linked Data.

In this tutorial, we will show how to work with Linked Data. Mainly, we will see how to work with population dataset.
We will look into how to query, process, and visualize it.

1. Population in city districts
2. Population origin
3. Population distribution: age and time
4. Population distribution: age and sex
5. Population distribution: age and origin
6. Population and real estate prices
7. Causes of death

SPARQL endpoint

Population data is published as Linked Data. It can be accessed with SPARQL queries.
You can send queries using HTTP requests. The API endpoint is https://ld.zazuko.com/query/.

Let's use SparqlClient from graphly to communicate with the database. Graphly will allow us to:

• send SPARQL queries
• automatically add prefixes to all queries
• format response to pandas or geopandas

# Installing dependencies for Colab environment
!pip install git+https://github.com/zazuko/graphly.git

import re

import pandas as pd
import numpy as np

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

from graphly.api_client import SparqlClient

ENDPOINT = "https://ld.zazuko.com/query/"

sparql = SparqlClient(ENDPOINT)
sparql.add_prefixes({
    "schema": "<http://schema.org/>",
    "cube": "<https://cube.link/>",
    "property": "<https://ld.stadt-zuerich.ch/statistics/property/>",
    "measure": "<https://ld.stadt-zuerich.ch/statistics/measure/>",
    "skos": "<http://www.w3.org/2004/02/skos/core#>",
    "ssz": "<https://ld.stadt-zuerich.ch/statistics/>"
})

SPARQL queries can become very long. To improve the readibility, we will work wih prefixes.

Using add_prefixes method, we define persistent prefixes. Every time you send a query, graphly will add automatically update the prefixes for you.

Population in city districts

Let's find the number of inhabitants in different parts of the city. The data on restaurants is available in BEW data cube.

The query for number of inhabitants in different city districts, over time is:

query = """
SELECT ?time ?place ?count
FROM <https://lindas.admin.ch/stadtzuerich/stat>
WHERE {
  ssz:BEW a cube:Cube;
             cube:observationSet/cube:observation ?observation.   
  
  ?observation property:RAUM ?place_uri ;
                       property:TIME ?time ;
                       measure:BEW ?count .
  ?place_uri skos:inScheme <https://ld.stadt-zuerich.ch/statistics/scheme/Kreis> ;
         schema:name ?place .
  FILTER regex(str(?place),"ab|Stadtgebiet vor")
}
ORDER BY ?time
"""

df = sparql.send_query(query)
df.head()

	time	place	count
0	1408-12-31	Kreis 1 (Stadtgebiet vor 1893)	5675.0
1	1467-12-31	Kreis 1 (Stadtgebiet vor 1893)	4750.0
2	1529-12-31	Kreis 1 (Stadtgebiet vor 1893)	5080.0
3	1637-12-31	Kreis 1 (Stadtgebiet vor 1893)	8621.0
4	1671-12-31	Kreis 1 (Stadtgebiet vor 1893)	9590.0

Let's visualize number of inhabitants per district. To do this, we will aggregate the prices per place.
The cleaned dataframe becomes:

df.place = df.place.apply(lambda x: re.findall('Kreis \d+', x)[0])

df = pd.pivot_table(df, index="time", columns="place", values="count")
df.dropna(inplace=True)

df = df[df.columns[np.argsort(-df.iloc[0,])]]
df = df.reset_index().rename_axis(None, axis=1)

df.head()

	time	Kreis 11	Kreis 3	Kreis 9	Kreis 7	Kreis 6	Kreis 10	Kreis 12	Kreis 2	Kreis 4	Kreis 8	Kreis 5	Kreis 1
0	1971-12-31	56863.0	52707.0	47257.0	39599.0	37837.0	36160.0	33664.0	32708.0	32231.0	20899.0	12833.0	9411.0
1	1972-12-31	56864.0	51674.0	47223.0	39118.0	37763.0	35760.0	33079.0	32561.0	31765.0	20371.0	12462.0	9007.0
2	1973-12-31	56464.0	50879.0	47215.0	38695.0	37059.0	35576.0	32201.0	31925.0	30906.0	19897.0	12235.0	8525.0
3	1974-12-31	56224.0	50175.0	47142.0	38045.0	36305.0	35449.0	31374.0	31706.0	30048.0	19552.0	12165.0	8076.0
4	1975-12-31	55627.0	49326.0	46491.0	37379.0	35294.0	35518.0	30943.0	31179.0	29061.0	19246.0	11798.0	7751.0

fig = px.line(df, x="time", y = df.columns)
fig.update_layout(
    title='Population in Zürich Districts', 
    title_x=0.5,
    yaxis_title="inhabitants",
    legend_title="District"
)
fig.show("notebook")

fig = px.histogram(df, x="time", y=df.columns, barnorm="percent")
fig.update_layout(
    title='Population in Zürich Districts', 
    title_x=0.5,
    yaxis_title="% of inhabitants",
    legend_title="District"
)
fig.show()

Population origin

Let's find the number of foreign and swiss inhabitants. The share of swiss/non-swiss population is available in ANT-GGH-HEL data cube. The population count is available in BEW data cube.

The query for number of inhabitants and foreigners share over time is:

query = """
SELECT ?time (SUM(?pop_count) AS ?pop) (SUM(?foreigners_count)/SUM(?pop_count) AS ?foreigners) 
FROM <https://lindas.admin.ch/stadtzuerich/stat>
WHERE {
  ssz:BEW a cube:Cube;
             cube:observationSet/cube:observation ?obs_bew.   
  ?obs_bew property:TIME ?time ;
           property:RAUM ?place_uri;
           measure:BEW ?pop_count .
  
  ssz:ANT-GGH-HEL a cube:Cube;
             cube:observationSet/cube:observation ?obs_ant.   
  
  ?obs_ant property:TIME ?time ;
           property:RAUM ?place_uri;
           measure:ANT ?ratio .
  
  ?place_uri skos:inScheme <https://ld.stadt-zuerich.ch/statistics/scheme/Kreis> ;
             schema:name ?place .
  
  BIND((?pop_count * ?ratio/100) AS ?foreigners_count)
}
GROUP BY ?time
ORDER BY ?time
"""

df = sparql.send_query(query)
df.head()

	time	pop	foreigners
0	1934-12-31	317367.0	0.116519
1	1935-12-31	318981.0	0.110071
2	1936-12-31	319849.0	0.102763
3	1937-12-31	321380.0	0.097606
4	1938-12-31	329780.0	0.100623

fig = make_subplots(rows=2, cols=1)

fig.append_trace(go.Scatter(x=df["time"],y=df["pop"],
        name="Total population", 
        marker_color=px.colors.qualitative.Vivid[7],
        showlegend=False,
    ), row=1, col=1)

fig.append_trace(go.Bar(x=df["time"],y=1-df["foreigners"],
        name="swiss", 
        marker_color=px.colors.qualitative.Vivid[3]
    ), row=2, col=1)


fig.append_trace(go.Bar(x=df["time"],y=df["foreigners"],
        name="foreign",
        marker_color=px.colors.qualitative.Vivid[9]
    ), row=2, col=1)

fig['layout']['yaxis']['title']='inhabitants'
fig['layout']['yaxis2']['title']='population share'
fig['layout']['yaxis2']['range']= [0,1]
fig.update_layout(height=800, title={"text": "Population in Zürich", "x": 0.5}, barmode = "stack",
                  legend = {"x": 1, "y": 0.37})
fig.show()

Population distribution: age and time

Let's find the number of inhabitants in different age groups. The population count per age group is available in BEW-ALT-HEL-SEX data cube.

The query for number of inhabitants in various age buckets over time is:

query = """
    SELECT ?time ?age (SUM(?measure) AS ?count) 
    FROM <https://lindas.admin.ch/stadtzuerich/stat>
    WHERE {
      ssz:BEW-ALT-HEL-SEX a cube:Cube;
                 cube:observationSet/cube:observation ?observation.   
      ?observation property:TIME ?time ;
                   property:ALT ?age_uri ;
                   measure:BEW ?measure .
      ?age_uri schema:name ?age .
      FILTER (!REGEX(?age, "–", "i"))
    }
    GROUP BY ?time ?age
    ORDER BY asc(?time)
"""

df = sparql.send_query(query)
df.head()

	time	age	count
0	2002-12-31	8 Jahre alt	2722.0
1	2002-12-31	61 Jahre alt	3490.0
2	2002-12-31	75 Jahre alt	2855.0
3	2002-12-31	92 Jahre alt	621.0
4	2002-12-31	58 Jahre alt	3961.0

Let's calculate the population share for each age group. The dataframe becomes:

df["year"] = df.time.apply(getattr, args=("year", ))
df["count"] = df.groupby(["year"]).transform(lambda x: (x/x.sum()))
df['age'] = df['age'].apply(lambda x: int(str(x.split(" ")[0])))

df = df.sort_values(by=["year", "age"]).reset_index(drop=True)
df.head()

	time	age	count	year
0	2002-12-31	0	0.009233	2002
1	2002-12-31	1	0.008727	2002
2	2002-12-31	2	0.008730	2002
3	2002-12-31	3	0.008245	2002
4	2002-12-31	4	0.007793	2002

fig = px.bar(df, x="age", y="count", animation_frame="year", range_y=[0, 0.025], range_x=[0, df.age.max()])
fig.update_layout(
    title='Population distribution', 
    title_x=0.5,
    yaxis_title="population share",
    legend_title="District"
)
fig.show()

Population distribution: age and origin

Let's take a look at age distribution among swiss and foreign inhabitants. We can find this data in BEW-ALT-HEL-SEX data cube.

The query for number of inhabitants in various age buckets, with their origin, over time is:

query = """
    SELECT ?age ?origin (SUM(?measure) AS ?count) 
    FROM <https://lindas.admin.ch/stadtzuerich/stat>
    WHERE {
      ssz:BEW-ALT-HEL-SEX a cube:Cube;
        cube:observationSet/cube:observation ?observation.   
        ?observation property:TIME ?time ;
        property:ALT/schema:name ?age;
        measure:BEW ?measure ;
        property:HEL/schema:name ?origin .

        FILTER (!REGEX(?age, "–", "i"))
        FILTER (?time = "2017-12-31"^^xsd:date)
    }
    GROUP BY ?age ?origin
    ORDER BY asc(?age)
"""

df = sparql.send_query(query)
df.head()

	age	origin	count
0	0 Jahre alt	Ausland	1659.0
1	0 Jahre alt	Schweiz	3316.0
2	1 Jahr alt	Ausland	1613.0
3	1 Jahr alt	Schweiz	3289.0
4	10 Jahre alt	Ausland	800.0

Let's calculate the population share for each origin and age group. The dataframe becomes:

df["age"] = df["age"].apply(lambda x: int(str(x.split(" ")[0])))
df["count"] = df[["origin", "count"]].groupby(["origin"]).transform(lambda x: x/x.sum())
df = df.sort_values(by=["age"]).reset_index(drop=True)
df.loc[df["origin"]=="Ausland", "origin"] = "foreign"
df.loc[df["origin"]=="Schweiz", "origin"] = "swiss"
df.head()

	age	origin	count
0	0	foreign	0.012100
1	0	swiss	0.011546
2	1	foreign	0.011765
3	1	swiss	0.011452
4	2	foreign	0.011203

fig = px.bar(df, x="age", y="count", 
             barmode="overlay", range_y = [0,0.035], color="origin")

fig.update_layout(
    title='Population distribution', 
    title_x=0.5,
    yaxis_title="population share",
    legend_title="Origin"
)
fig.show()

Population distribution: age and sex

Let's take a look at age distribution for felame and male inhabitants. We can find this data in BEW-ALT-HEL-SEX data cube.

The query for number of inhabitants in various age buckets, with their sex, over time is:

query = """
SELECT ?time ?sex ?age (SUM(?measure) AS ?count) 
FROM <https://lindas.admin.ch/stadtzuerich/stat>
WHERE {
  ssz:BEW-ALT-HEL-SEX a cube:Cube;
    cube:observationSet/cube:observation ?observation.   
    ?observation property:TIME ?time ;
    measure:BEW ?measure ;
    property:SEX/schema:name ?sex ;
    property:ALT/schema:name ?age.
    
    FILTER (!REGEX(?age, "–", "i"))
}
GROUP BY ?time ?sex ?age
ORDER BY asc(?time)
"""

df = sparql.send_query(query)
df.head()

	time	sex	age	count
0	2002-12-31	weiblich	55 Jahre alt	2127.0
1	2002-12-31	männlich	3 Jahre alt	1589.0
2	2002-12-31	männlich	31 Jahre alt	4038.0
3	2002-12-31	weiblich	99 Jahre und älter	99.0
4	2002-12-31	weiblich	5 Jahre alt	1314.0

Let's create a dataframe where one row represents one observation. It will allow us to use violin plots for our dataframe.
The dataframe becomes:

df.loc[df["sex"]=="weiblich", "sex"] = "female"
df.loc[df["sex"]=="männlich", "sex"] = "male"

df['age'] = df['age'].apply(lambda x: str(x.split(" ")[0])).astype(int)
df["year"] = df.time.apply(getattr, args=("year", )).astype(str)
df = df.sort_values(by=["year", "age"]).reset_index(drop=True)

df = df[(df.year == df.year.max()) | ((df.year == df.year.min()))]
df = df[["sex", "age", "year"]].loc[df.index.repeat(df["count"])].reset_index(drop=True)
df.head()

	sex	age	year
0	female	0	2002
1	female	0	2002
2	female	0	2002
3	female	0	2002
4	female	0	2002

fig = px.violin(df, y="age", x="year", color="sex", violinmode="overlay")
fig.data[0].update(span = [0, 105], spanmode='manual')
fig.data[1].update(span = [0, 105], spanmode='manual')
fig.update_layout(title={"text": "Population distrubution", "x": 0.5})
fig.show()

fig = go.Figure()

fig.add_trace(go.Violin(x=df['sex'][df['year'] == "2002"],
                        y=df['age'][df['year'] == "2002"],
                        legendgroup='2002', scalegroup='2002', name='2002',
                        side='negative',
                        line_color='blue', 
                        span = [0, 105], 
                        spanmode='manual'))

fig.add_trace(go.Violin(x=df['sex'][df['year'] == "2017"],
                        y=df['age'][df['year'] == "2017"],
                        legendgroup='2017', scalegroup='2017', name='2017',
                        side='positive',
                        line_color='orange',
                        span = [0, 105], 
                        spanmode='manual'))

fig.update_traces(meanline_visible=True)
fig.update_layout(title={"text": "Population distrubution", "x": 0.5})
fig.show()

Population and real estate prices

Let's compare real estate prices and number of inhabitants over time. We will need to work with population and real estate data sets. The population data is available in BEW data cube. The real estate prices are in QMP-EIG-HAA-OBJ-ZIM data cube.

The query for number of inhabitants and housing prices over time is:

query="""
SELECT * 
FROM <https://lindas.admin.ch/stadtzuerich/stat>
WHERE{ 
    {
      SELECT ?time (SUM(?pop_count) AS ?pop)
      WHERE {
        ssz:BEW a cube:Cube;
                   cube:observationSet/cube:observation ?obs_bew.   
        ?obs_bew property:TIME ?time ;
                 property:RAUM ?place_uri_pop;
                 measure:BEW ?pop_count .

        ?place_uri_pop skos:inScheme <https://ld.stadt-zuerich.ch/statistics/scheme/Kreis> ;
                   schema:name ?place_pop .

        FILTER regex(str(?place_pop),"ab|Stadtgebiet vor")
      }
      GROUP BY ?time
    }
    {
      SELECT ?time (AVG(?quote) AS ?price)
      WHERE {
        ssz:QMP-EIG-HAA-OBJ-ZIM a cube:Cube;
                   cube:observationSet/cube:observation ?obs_apt.   
        ?obs_apt property:TIME ?time ;
                             property:RAUM ?place_uri_apt;
                             measure:QMP ?quote .  

        ?place_uri_apt skos:inScheme <https://ld.stadt-zuerich.ch/statistics/scheme/Kreis> ;
                   schema:name ?place_apt .

        FILTER (?quote > 0)
        FILTER regex(str(?place_apt),"ab|Stadtgebiet vor")
      }
      GROUP BY ?time
    }
}
"""

df = sparql.send_query(query)
df.head()

	time	pop	price
0	2009-12-31	382906.0	8702.231884
1	2010-12-31	385468.0	9206.983871
2	2011-12-31	390082.0	10061.363636
3	2012-12-31	394012.0	11442.333333
4	2013-12-31	398575.0	11096.770492

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Scatter(x=df["time"], y=df["pop"], name="inhabitants"),
    secondary_y=False,
)

fig.add_trace(
    go.Scatter(x=df["time"], y=df["price"], name="price per m<sup>2</sup>"),
    secondary_y=True,
)

# Layout
fig.update_layout(title={"text": "Population and real estate prices", "x": 0.5})
fig.update_yaxes(title_text="population", secondary_y=False)
fig.update_yaxes(title_text="price per m<sup>2</sup>", secondary_y=True)
fig.show()

Causes of death

Statistical Office reports number of deaths, and its cause. Let's try to understand what are are main causes of death in Zurich. This data is available in GES-SEX-TOU data cube.

The query for death cause and its broader category is:

query = """
SELECT ?tou ?tou_broader (SUM(?ges) AS ?deaths)
FROM <https://lindas.admin.ch/stadtzuerich/stat>
WHERE {
  ssz:GES-SEX-TOU a cube:Cube;
                  cube:observationSet/cube:observation ?obs.   
  ?obs property:TIME ?time ;                           
       property:TOU ?tou_uri;
                   measure:GES ?ges .
  
  ?tou_uri schema:name ?tou ;
     skos:broader/schema:name ?tou_broader .

  MINUS {?three_level_tou skos:broader ?tou_uri .}
  FILTER (?time = "2015-12-31"^^xsd:date)
}
GROUP BY ?tou ?tou_broader
HAVING (?deaths > 0)
ORDER BY ?tou_broader
"""

df = sparql.send_query(query)
df.head()

	tou	tou_broader	deaths
0	Chronische Bronchitis	Atmungsorgane	53.0
1	andere Atmungsorgane	Atmungsorgane	19.0
2	Pneumonie / Lungenentzündung	Atmungsorgane	39.0
3	Asthma	Atmungsorgane	1.0
4	Grippe	Atmungsorgane	3.0

Let's aggregate those results under more meaningful groups names.

df.loc[(df.tou == "andere infektiöse Krankheiten"), "tou_broader"] = ""
df.loc[(df.tou == "andere infektiöse Krankheiten"), "tou"] = "Infektiöse Krankheiten"
df.loc[(df.tou == "Alkoholische Leberzirrhose"), "tou_broader"] = ""
df.loc[(df.tou == "Unbekannt"), "deaths"] = df.loc[(df.tou_broader == "Übrige"), "deaths"].sum()
df.loc[(df.tou == "Unbekannt"), "tou_broader"] = ""
df.loc[(df.tou_broader == "Krebskrankheiten/Bösartige Neubildungen"), "tou_broader"] = "Krebskrankheiten"

df = df.drop(df[df.tou == "Übrige (ohne unbekannte Todesursachen)"].index)

fig = px.treemap(df, path=['tou_broader', "tou"], values='deaths')
fig.update_layout(title={"text": "Causes of death", "x": 0.5})
fig.show()